The modern practice of medicine poses a number of challenges for clinicians to effectively deliver quality care to patients. In particular, the effective medical knowledge base continues to grow at a rapid pace, making it difficult for clinicians to keep up with and carry out recognized best practices. For instance, thousands of new journal articles are published each month providing a plethora of new evidence-based clinical information. Additionally, new drugs, treatment techniques, and testing procedures are continuously being researched and developed. The difficulty for clinicians to keep appraised of such information is exacerbated by the fact that clinicians are typically pulled in many different directions by a vast number of patients. Moreover, clinicians must often make quick decisions regarding patient treatment. As a result, there currently exists a gap between recognized best practices and actual clinician practices. This gap contributes to decreased quality of care, increased risk of medical errors, and increased cost of healthcare.
Over the past decade, there has been an increased use of computers to assist clinicians in the clinical care process. In particular, clinical decision support systems have been developed to address the gap between evidenced best practices and actual clinician practices by assisting clinicians in the delivery of care. Generally, clinical decision support systems may provide point-of-care case-specific clinical advice based on clinical information for a patient and a clinical knowledge base.
Different types of clinical decision support systems are available that may support various aspects of the clinical care process, such as clinical diagnosis and treatment planning, thereby advancing clinicians' use of best practices. In one form, currently available clinical decision support systems provide decision support through advice and alerts that are triggered based on stored clinical information. A clinical decision support system of this type monitors clinical information, such as information stored in a patient's electronic medical record, and compares the clinical information against a knowledge base, which may include different sets of algorithms and rules for providing decision support. When clinical information for a patient satisfies a rule or set of rules, an alert or other piece of advice is provided to a clinician. However, this type of clinical decision support system provides only a reactive approach to decision support. Clinical advice is provided only if an existing condition is detected based on available clinical information for a patient. Clinicians may not use this type of system to proactively evaluate patients' conditions and develop treatment plans. Moreover, a particular rule is triggered and advice is provided only if clinical information required for the rule is stored and available to the system. For example, a rule may require ten pieces of clinical information to determine whether an alert should be provided. If only nine pieces of clinical information are available to the system, a determination for the rule cannot be made. Another shortcoming of this type of clinical decision support system is that it relies solely upon objective information to provide decision support. However, many clinical decisions require consideration of subjective factors.
In another form, currently available clinical decision support systems may operate as collection devices to gather clinical information from clinicians for decision support. This type of clinical decision support system provides an interactive approach as it solicits clinical information regarding a patient from a clinician and uses the solicited information to navigate through decision trees and generate clinical advice. However, this type of clinical decision support system is typically provided as a stand-alone system and is not tied to stored clinical information, such as information stored by an electronic medical record. Accordingly, the system does not automatically monitor clinical information and determine when a particular condition is present and/or a particular clinical action may be appropriate. Instead, a clinician must manually select and walk through a clinical decision support event. Additionally, a clinician must manually provide all clinical information used by the system. This may require the clinician to manually look-up clinical information, such as laboratory testing results, which may be a time-consuming process.